[1]} is proposed to derive a general-purpose backbone based on ViT, which can be used for different applications, e.g., image classification and semantic segmentation. Since applying self-attention pixel-by-pixel results in a tremendous computation complexity, Swin-Transformer forms a hierarchical structure, which merges patches after each Swin-Transformer block and therefore has approximate linear computation time complexity to input image size due to the computation of self-attention only within each local shifted window.
